OpenRaiser/Intern-Atlas huggingface dataset:
Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
Venue: arXiv cs.LG 2025
Year: 2025

We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, where experts operate independently in parallel, CoE processes tokens iteratively across a chain of experts inside a layer. To support dynamic expert selection across iterations, CoE employs a dedicated router at each iteration step within a layer. This design allows tokens to re-evaluate and select different experts during each iteration, rather than being statically assigned. As a result, CoE introduces a flexible routing mechanism that increases the diversity of expert combinations and enriches the model's representational capacity. CoE demonstrates improved performance under fixed compute: on math reasoning tasks, it reduces validation loss from 1.20 to 1.12 compared to a standard MoE. Beyond performance, CoE offers a new scaling axis: depth through expert iteration, which complements conventional width/depth scaling. For example, using 2x iterations matches the performance of 3x expert selections (in width), while reducing memory usage by 17.6-42% relative to other scaling strategies. Our analysis reveals that CoE's benefits stem from its iterative residual structure and enhanced expert specialization empowered by iterative routing, which together unlock more expressive representations. Code is available at https://github.com/ZihanWang314/coe.
Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) often generate responses with inherent biases, undermining their reliability in real-world applications. Existing evaluation methods often overlook biases in long-form responses and the intrinsic variability of LLM outputs. To address these challenges, we propose FiSCo (Fine-grained Semantic Comparison), a novel statistical framework to evaluate group-level fairness in LLMs by detecting subtle semantic differences in long-form responses across demographic groups. Unlike prior work focusing on sentiment or token-level comparisons, FiSCo goes beyond surface-level analysis by operating at the claim level, leveraging entailment checks to assess the consistency of meaning across responses. We decompose model outputs into semantically distinct claims and apply statistical hypothesis testing to compare inter- and intra-group similarities, enabling robust detection of subtle biases. We formalize a new group counterfactual fairness definition and validate FiSCo on both synthetic and human-annotated datasets spanning gender, race, and age. Experiments show that FiSCo more reliably identifies nuanced biases while reducing the impact of stochastic LLM variability, outperforming various evaluation metrics.
Enhancing Evacuation Safety: Detecting Post-Nuclear Event Radiation Levels in an Urban Area
Venue: arXiv cs.NI 2025
Year: 2025

The detonation of an improvised nuclear device (IND) in an urban area would cause catastrophic damage, followed by hazardous radioactive fallout. Timely dissemination of radiation data is crucial for evacuation and casualty reduction. However, conventional communication infrastructure is likely to be severely disrupted. This study designs and builds a pseudorealistic, geospatially and temporally dynamic post-nuclear event (PNE) scenario using the Opportunistic Network Environment (ONE) simulator. It integrates radiation sensing by emergency responders, unmanned aerial vehicles (UAVs), and civilian devices as dynamic nodes within Delay-Tolerant Networks (DTNs). The performance of two DTN routing protocols, Epidemic and PRoPHET, was evaluated across multiple PNE phases. Both protocols achieve high message delivery rates, with PRoPHET exhibiting lower network overhead but higher latency. Findings demonstrate the potential of DTN-based solutions to support emergency response and evacuation safety by ensuring critical radiation data propagation despite severe infrastructure damage.
LEGATO: Large-scale End-to-end Generalizable Approach to Typeset OMR
Venue: arXiv cs.CV 2025
Year: 2025

We propose Legato, a new end-to-end model for optical music recognition (OMR), a task of converting music score images to machine-readable documents. Legato is the first large-scale pretrained OMR model capable of recognizing full-page or multi-page typeset music scores and the first to generate documents in ABC notation, a concise, human-readable format for symbolic music. Bringing together a pretrained vision encoder with an ABC decoder trained on a dataset of more than 214K images, our model exhibits the strong ability to generalize across various typeset scores. We conduct comprehensive experiments on a range of datasets and metrics and demonstrate that Legato outperforms the previous state of the art. On our most realistic dataset, we see a 68\% and 47.6\% absolute error reduction on the standard metrics TEDn and OMR-NED, respectively.
Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms
Venue: arXiv cs.DC 2025
Year: 2025

A unit ball graph consists of a set of vertices, labeled by points in Euclidean space, and edges joining all pairs of points within distance 1. These geometric graphs are used to model a variety of spatial networks, including communication networks between agents in an autonomous swarm. In such an application, vertices and/or edges of the graph may not be perfectly reliable; an agent may experience failure or a communication link rendered inoperable. With the goal of designing robust swarm formations, or unit ball graphs with high reliability (probability of connectedness), in a preliminary conference paper we provided an algorithm with cubic time complexity to determine all possible changes to a unit ball graph by repositioning a single vertex. Using this algorithm and Monte Carlo simulations, one obtains an efficient method to modify a unit ball graph by moving a single vertex to a location which maximizes the reliability. Another important consideration in many swarm missions is area coverage, yet highly reliable ball graphs often contain clusters of vertices. Here, we generalize our previous algorithm to improve area coverage as well as reliability. Our algorithm determines a location to add or move a vertex within a unit ball graph which maximizes the reliability, under the constraint that no other vertices of the graph be within some fixed distance. We compare this method of obtaining graphs with high reliability and evenly distributed area coverage to another method which uses a modified Fruchterman-Reingold algorithm for ball graphs.
A Robust Method for Pitch Tracking in the Frequency Following Response using Harmonic Amplitude Summation Filterbank
Venue: arXiv cs.SD 2025
Year: 2025

The Frequency Following Response (FFR) reflects the brain's neural encoding of auditory stimuli including speech. Because the fundamental frequency (F0), a physical correlate of pitch, is one of the essential features of speech, there has been particular interest in characterizing the FFR at F0, especially when F0 varies over time. The standard method for extracting F0 in FFRs has been the Autocorrelation Function (ACF). This paper investigates harmonic-structure-based F0 estimation algorithms, originally developed for speech and music, and resolves their poor performance when applied to FFRs in two steps. Firstly, given that unlike in speech or music, stimulus F0 of FFRs is already known, we introduce a stimulus-aware filterbank that selectively aggregates amplitudes at F0 and its harmonics while suppressing noise at non-harmonic frequencies. This method, called Harmonic Amplitude Summation (HAS), evaluates F0 candidates only within a range centered around the stimulus F0. Secondly, unlike other pitch tracking methods that select the highest peak, our method chooses the most prominent one, as it better reflects the underlying periodicity of FFRs. To the best of our knowledge, this is the first study to propose an F0 estimation algorithm for FFRs that relies on harmonic structure. Analyzing recorded FFRs from 16 normal hearing subjects to 4 natural speech stimuli with a wide F0 variation from 89 Hz to 452 Hz showed that this method outperformed ACF by reducing the average Root-Mean-Square-Error (RMSE) within each response and stimulus F0 contour pair by 8.8% to 47.4%, depending on the stimulus.
MSR-Align: Policy-Grounded Multimodal Alignment for Safety-Aware Reasoning in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have achieved remarkable progress in multimodal reasoning tasks through enhanced chain-of-thought capabilities. However, this advancement also introduces novel safety risks, as these models become increasingly vulnerable to harmful multimodal prompts that can trigger unethical or unsafe behaviors. Existing safety alignment approaches, primarily designed for unimodal language models, fall short in addressing the complex and nuanced threats posed by multimodal inputs. Moreover, current safety datasets lack the fine-grained, policy-grounded reasoning required to robustly align reasoning-capable VLMs. In this work, we introduce {MSR-Align}, a high-quality Multimodal Safety Reasoning dataset tailored to bridge this gap. MSR-Align supports fine-grained, deliberative reasoning over standardized safety policies across both vision and text modalities. Our data generation pipeline emphasizes multimodal diversity, policy-grounded reasoning, and rigorous quality filtering using strong multimodal judges. Extensive experiments demonstrate that fine-tuning VLMs on MSR-Align substantially improves robustness against both textual and vision-language jailbreak attacks, while preserving or enhancing general reasoning performance. MSR-Align provides a scalable and effective foundation for advancing the safety alignment of reasoning-capable VLMs. Our dataset is made publicly available at https://huggingface.co/datasets/Leigest/MSR-Align.
Style Transfer: A Decade Survey
Venue: arXiv cs.GR 2025
Year: 2025

The revolutionary advancement of Artificial Intelligence Generated Content (AIGC) has fundamentally transformed the landscape of visual content creation and artistic expression. While remarkable progress has been made in image generation and style transfer, the underlying mechanisms and aesthetic implications of these technologies remain insufficiently understood. This paper presents a comprehensive survey of AIGC technologies in visual arts, tracing their evolution from early algorithmic frameworks to contemporary deep generative models. We identify three pivotal paradigms: Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), and Diffusion Models, and examine their roles in bridging the gap between human creativity and machine synthesis. To support our analysis, we systematically review over 500 research papers published in the past decade, spanning both foundational developments and state-of-the-art innovations. Furthermore, we propose a multidimensional evaluation framework that incorporates Technical Innovation, Artistic Merit, Visual Quality, Computational Efficiency, and Creative Potential. Our findings reveal both the transformative capacities and current limitations of AIGC systems, emphasizing their profound impact on the future of creative practices. Through this extensive synthesis, we offer a unified perspective on the convergence of artificial intelligence and artistic expression, while outlining key challenges and promising directions for future research in this rapidly evolving field.
EmoStage: A Framework for Accurate Empathetic Response Generation via Perspective-Taking and Phase Recognition
Venue: arXiv cs.CL 2025
Year: 2025

The rising demand for mental health care has fueled interest in AI-driven counseling systems. While large language models (LLMs) offer significant potential, current approaches face challenges, including limited understanding of clients' psychological states and counseling stages, reliance on high-quality training data, and privacy concerns associated with commercial deployment. To address these issues, we propose EmoStage, a framework that enhances empathetic response generation by leveraging the inference capabilities of open-source LLMs without additional training data. Our framework introduces perspective-taking to infer clients' psychological states and support needs, enabling the generation of emotionally resonant responses. In addition, phase recognition is incorporated to ensure alignment with the counseling process and to prevent contextually inappropriate or inopportune responses. Experiments conducted in both Japanese and Chinese counseling settings demonstrate that EmoStage improves the quality of responses generated by base models and performs competitively with data-driven methods.
Capturing Fine-Grained Alignments Improves 3D Affordance Detection
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we address the challenge of affordance detection in 3D point clouds, a task that requires effectively capturing fine-grained alignments between point clouds and text. Existing methods often struggle to model such alignments, resulting in limited performance on standard benchmarks. A key limitation of these approaches is their reliance on simple cosine similarity between point cloud and text embeddings, which lacks the expressiveness needed for fine-grained reasoning. To address this limitation, we propose LM-AD, a novel method for affordance detection in 3D point clouds. Moreover, we introduce the Affordance Query Module (AQM), which efficiently captures fine-grained alignment between point clouds and text by leveraging a pretrained language model. We demonstrated that our method outperformed existing approaches in terms of accuracy and mean Intersection over Union on the 3D AffordanceNet dataset.
Progressive Modality Cooperation for Multi-Modality Domain Adaptation
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we propose a new generic multi-modality domain adaptation framework called Progressive Modality Cooperation (PMC) to transfer the knowledge learned from the source domain to the target domain by exploiting multiple modality clues (\eg, RGB and depth) under the multi-modality domain adaptation (MMDA) and the more general multi-modality domain adaptation using privileged information (MMDA-PI) settings. Under the MMDA setting, the samples in both domains have all the modalities. In two newly proposed modules of our PMC, the multiple modalities are cooperated for selecting the reliable pseudo-labeled target samples, which captures the modality-specific information and modality-integrated information, respectively. Under the MMDA-PI setting, some modalities are missing in the target domain. Hence, to better exploit the multi-modality data in the source domain, we further propose the PMC with privileged information (PMC-PI) method by proposing a new multi-modality data generation (MMG) network. MMG generates the missing modalities in the target domain based on the source domain data by considering both domain distribution mismatch and semantics preservation, which are respectively achieved by using adversarial learning and conditioning on weighted pseudo semantics. Extensive experiments on three image datasets and eight video datasets for various multi-modality cross-domain visual recognition tasks under both MMDA and MMDA-PI settings clearly demonstrate the effectiveness of our proposed PMC framework.
Training-Free Motion Customization for Distilled Video Generators with Adaptive Test-Time Distillation
Venue: arXiv cs.CV 2025
Year: 2025

Distilled video generation models offer fast and efficient synthesis but struggle with motion customization when guided by reference videos, especially under training-free settings. Existing training-free methods, originally designed for standard diffusion models, fail to generalize due to the accelerated generative process and large denoising steps in distilled models. To address this, we propose MotionEcho, a novel training-free test-time distillation framework that enables motion customization by leveraging diffusion teacher forcing. Our approach uses high-quality, slow teacher models to guide the inference of fast student models through endpoint prediction and interpolation. To maintain efficiency, we dynamically allocate computation across timesteps according to guidance needs. Extensive experiments across various distilled video generation models and benchmark datasets demonstrate that our method significantly improves motion fidelity and generation quality while preserving high efficiency. Project page: https://euminds.github.io/motionecho/
SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation
Venue: arXiv cs.CR 2025
Year: 2025

Advances in generative models have transformed the field of synthetic image generation for privacy-preserving data synthesis (PPDS). However, the field lacks a comprehensive survey and comparison of synthetic image generation methods across diverse settings. In particular, when we generate synthetic images for the purpose of training a classifier, there is a pipeline of generation-sampling-classification which takes private training as input and outputs the final classifier of interest. In this survey, we systematically categorize existing image synthesis methods, privacy attacks, and mitigations along this generation-sampling-classification pipeline. To empirically compare diverse synthesis approaches, we provide a benchmark with representative generative methods and use model-agnostic membership inference attacks (MIAs) as a measure of privacy risk. Through this study, we seek to answer critical questions in PPDS: Can synthetic data effectively replace real data? Which release strategy balances utility and privacy? Do mitigations improve the utility-privacy tradeoff? Which generative models perform best across different scenarios? With a systematic evaluation of diverse methods, our study provides actionable insights into the utility-privacy tradeoffs of synthetic data generation methods and guides the decision on optimal data releasing strategies for real-world applications.
The Role of Fractal Dimension in Wireless Mesh Network Performance
Venue: arXiv cs.NI 2025
Year: 2025

Wireless mesh networks (WMNs) depend on the spatial distribution of nodes, which directly influences connectivity, routing efficiency, and overall network performance. Conventional models typically assume uniform or random node placement, which inadequately represent the complex, hierarchical spatial patterns observed in practical deployments. In this study, we present a novel algorithm that constructs WMN topologies with tunable fractal dimensions, allowing precise control over spatial self-similarity. By systematically varying the fractal dimension, the algorithm generates network layouts spanning a continuum of spatial complexities, ranging from sparse fragmented clusters to dense, cohesive structures. Through NS-3 simulations, Key performance metrics including throughput, latency, jitter, and packet delivery ratio were evaluated across a range of fractal dimensions. Comparative evaluations against classical random, small-world, scale-free, grid and hierarchical tree networks models reveal that high-dimensional fractal topologies achieve enhanced resilience and throughput under equivalent conditions. These findings demonstrate the potential of fractal geometry as a design paradigm for scalable and efficient WMN architectures.
An ETSI GS QKD compliant TLS implementation
Venue: arXiv cs.CR 2025
Year: 2025

A modification of the TLS protocol is presented, using our implementation of the Quantum Key Distribution (QKD) standard ETSI GS QKD 014 v1.1.1. We rely on the Rustls library for this. The TLS protocol is modified while maintaining backward compatibility on the client and server side. We thus wish to participate in the effort to generalize the use of QKD on the Internet. We used our protocol for a video conference call encrypted by QKD. Finally, we analyze the performance of our protocol, comparing the time needed to establish a handshake to that of TLS 1.3.
Virtual Memory for 3D Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussian Splatting represents a breakthrough in the field of novel view synthesis. It establishes Gaussians as core rendering primitives for highly accurate real-world environment reconstruction. Recent advances have drastically increased the size of scenes that can be created. In this work, we present a method for rendering large and complex 3D Gaussian Splatting scenes using virtual memory. By leveraging well-established virtual memory and virtual texturing techniques, our approach efficiently identifies visible Gaussians and dynamically streams them to the GPU just in time for real-time rendering. Selecting only the necessary Gaussians for both storage and rendering results in reduced memory usage and effectively accelerates rendering, especially for highly complex scenes. Furthermore, we demonstrate how level of detail can be integrated into our proposed method to further enhance rendering speed for large-scale scenes. With an optimized implementation, we highlight key practical considerations and thoroughly evaluate the proposed technique and its impact on desktop and mobile devices.
KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces KunLunBaizeRAG, a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex multi-hop question-answering tasks. The framework addresses key limitations of traditional RAG, such as retrieval drift, information redundancy, and strategy rigidity. Key innovations include the RAG-driven Reasoning Alignment (RDRA) mechanism, the Search-Think Iterative Enhancement (STIE) mechanism, the Network-Local Intelligent Routing (NLR) mechanism, and a progressive hybrid training strategy. Experimental results demonstrate significant improvements in exact match (EM) and LLM-judged score (LJ) across four benchmarks, highlighting the framework's robustness and effectiveness in complex reasoning scenarios.
Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, significant progress has been made in the field of surgical scene understanding, particularly in the task of Visual Question Localized-Answering in robotic surgery (Surgical-VQLA). However, existing Surgical-VQLA models lack deep reasoning capabilities and interpretability in surgical scenes, which limits their reliability and potential for development in clinical applications. To address this issue, inspired by the development of Reasoning Multimodal Large Language Models (MLLMs), we first build the Surgery-R1-54k dataset, including paired data for Visual-QA, Grounding-QA, and Chain-of-Thought (CoT). Then, we propose the first Reasoning MLLM for Surgical-VQLA (Surgery-R1). In our Surgery-R1, we design a two-stage fine-tuning mechanism to enable the basic MLLM with complex reasoning abilities by utilizing supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT). Furthermore, for an efficient and high-quality rule-based reward system in our RFT, we design a Multimodal Coherence reward mechanism to mitigate positional illusions that may arise in surgical scenarios. Experiment results demonstrate that Surgery-R1 outperforms other existing state-of-the-art (SOTA) models in the Surgical-VQLA task and widely-used MLLMs, while also validating its reasoning capabilities and the effectiveness of our approach. The code and dataset will be organized in https://github.com/FiFi-HAO467/Surgery-R1.